
Discover strategies for industrializing GenAI operations, from provisioning to risk management, ensuring trusted solutions that deliver business outcomes on the Databricks platform.

Explore next-gen data modeling strategies for large-scale systems. Learn about YODA, an internal tool combining developer experience, DBT, Databricks, and more, to address data standardization and governance challenges.

Explore the evolution of data warehousing from traditional ETL to modern Lakehouse architecture, focusing on Delta Lake, Structured Streaming, and PySpark implementation with practical code examples.

Discover how Databricks revolutionizes data processing by combining data warehouse and lake architectures through Delta Lake, Apache Spark, MLFlow, and Koalas frameworks for scalable data engineering and analytics.

Discover how to effectively manage data lakes using modern architectures, open table formats, and version control systems through practical examples with Databricks, Apache Iceberg, and AWS technologies.

Explore MLflow and Databricks for production ML model deployment. Learn end-to-end management, model tracking, and deployment strategies, plus Databricks' Feature Store and AutoML.

Explore advanced geospatial analysis using Databricks. Learn to process spatial data at scale, integrate GIS with big data analytics, and utilize the H3 index for efficient grid-based operations.

Learn to manage ML experiments, reproduce pipelines, and deploy models using MLflow, an open-source tool for streamlining the machine learning lifecycle.

Discover data governance essentials and Unity Catalog's role in simplifying management for lakehouse analytics and AI initiatives. Learn key requirements and best practices.

Master Apache Spark Structured APIs for data manipulation using Databricks. Learn RDD, DataFrame API, data aggregation, and ETL operations in this comprehensive overview.

Explore Delta Lake's optimized data storage in Databricks, enhancing query performance and enabling time travel while reducing system complexity.

Explore Data Mesh principles for decentralized data management, empowering teams and ensuring data quality across organizations. Learn implementation on GCP and Databricks.

Explore a insightful conversation with Matei Zaharia, creator of Apache Spark, on open-source success, MLflow development, and the future of AI applications combining LLMs with other systems.

Explore emerging techniques in LLMOps for building, tuning, and maintaining high-quality LLM applications. Learn about testing tools, programming frameworks, and methods to control LLM outputs for desired goals.

Navigate the iterative development of LLM applications, addressing unique challenges in LLMOps design. Learn strategies for overcoming obstacles and adapting to the evolving landscape of large language model operations.

Explore LLMs using HuggingFace Transformers for summarization, translation, sentiment analysis, and advanced prompting techniques on Databricks.

Insights into Databricks Terraform provider's evolution, development challenges, and user anti-patterns from a maintainer's perspective, highlighting growth and lessons learned.

Explore LLMs and Generative AI's potential to revolutionize industries. Learn about Databricks Mosaic ML Training Platform, DBRX models, and LLMOps for streamlined AI adoption and innovation in healthcare and insurance.

Explore how the Texas Rangers leveraged Databricks' Data Intelligence Platform and GenAI for strategic insights, predictive analytics, and World Series victory.

Discover how KPMG leverages Databricks and Informatica to build trusted AI models, addressing data quality, governance, and ethical challenges for data-driven decision-making and business success.

Hands-on tutorial for building real-time data pipelines using Databricks, from ingesting NASA supernova data to implementing LLMs with RAG. Covers ETL, stream processing, and natural language analytics.